Vincent Fortuin

research

∙ 09/14/2023

Hodge-Aware Contrastive Learning

Simplicial complexes prove effective in modeling data with multiway depe...

0 Alexander Möllers, et al. ∙

research

∙ 06/29/2023

Understanding Pathologies of Deep Heteroskedastic Regression

Several recent studies have reported negative results when using heteros...

0 Eliot Wong-Toi, et al. ∙

research

∙ 05/26/2023

Laplace-Approximated Neural Additive Models: Improving Interpretability with Bayesian Inference

Deep neural networks (DNNs) have found successful applications in many f...

0 Kouroche Bouchiat, et al. ∙

research

∙ 04/17/2023

Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization

The linearized-Laplace approximation (LLA) has been shown to be effectiv...

0 Agustinus Kristiadi, et al. ∙

research

∙ 04/04/2023

Incorporating Unlabelled Data into Bayesian Neural Networks

We develop a contrastive framework for learning better prior distributio...

0 Mrinank Sharma, et al. ∙

research

∙ 11/14/2022

PAC-Bayesian Meta-Learning: From Theory to Practice

Meta-Learning aims to accelerate the learning on new tasks by acquiring ...

0 Jonas Rothfuss, et al. ∙

research

∙ 02/22/2022

Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations

Data augmentation is commonly applied to improve performance of deep lea...

58 Alexander Immer, et al. ∙

research

∙ 10/15/2021

Probing as Quantifying the Inductive Bias of Pre-trained Representations

Pre-trained contextual representations have led to dramatic performance ...

0 Alexander Immer, et al. ∙

research

∙ 10/08/2021

Pathologies in priors and inference for Bayesian transformers

In recent years, the transformer has established itself as a workhorse i...

0 Tristan Cinquin, et al. ∙

research

∙ 10/07/2021

Sparse MoEs meet Efficient Ensembles

Machine learning models based on the aggregated outputs of submodels, ei...

3 James Urquhart Allingham, et al. ∙

research

∙ 10/06/2021

Deep Classifiers with Label Noise Modeling and Distance Awareness

Uncertainty estimation in deep learning has recently emerged as a crucia...

0 Vincent Fortuin, et al. ∙

research

∙ 07/22/2021

Neural Variational Gradient Descent

Particle-based approximate Bayesian inference approaches such as Stein V...

0 Lauro Langosco di Langosco, et al. ∙

research

∙ 07/20/2021

A Bayesian Approach to Invariant Deep Neural Networks

We propose a novel Bayesian neural network architecture that can learn i...

0 Nikolaos Mourdoukoutas, et al. ∙

research

∙ 06/22/2021

Repulsive Deep Ensembles are Bayesian

Deep ensembles have recently gained popularity in the deep learning comm...

0 Francesco D'Angelo, et al. ∙

research

∙ 06/20/2021

On Stein Variational Neural Network Ensembles

Ensembles of deep neural networks have achieved great success recently, ...

0 Francesco D'Angelo, et al. ∙

research

∙ 06/10/2021

Data augmentation in Bayesian neural networks and the cold posterior effect

Data augmentation is a highly effective approach for improving performan...

0 Seth Nabarro, et al. ∙

research

∙ 05/14/2021

BNNpriors: A library for Bayesian neural network inference with different prior distributions

Bayesian neural networks have shown great promise in many applications w...

0 Vincent Fortuin, et al. ∙

research

∙ 05/14/2021

Priors in Bayesian Deep Learning: A Review

While the choice of prior is one of the most critical parts of the Bayes...

0 Vincent Fortuin, et al. ∙

research

∙ 04/11/2021

Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning

Marginal-likelihood based model-selection, even though promising, is rar...

0 Alexander Immer, et al. ∙

research

∙ 02/12/2021

Bayesian Neural Network Priors Revisited

Isotropic Gaussian priors are the de facto standard for modern Bayesian ...

16 Vincent Fortuin, et al. ∙

research

∙ 02/10/2021

On Disentanglement in Gaussian Process Variational Autoencoders

Complex multivariate time series arise in many fields, ranging from comp...

0 Simon Bing, et al. ∙

research

∙ 02/02/2021

Exact Langevin Dynamics with Stochastic Gradients

Stochastic gradient Markov Chain Monte Carlo algorithms are popular samp...

0 Adrià Garriga-Alonso, et al. ∙

research

∙ 01/24/2021

Annealed Stein Variational Gradient Descent

Particle based optimization algorithms have recently been developed as s...

0 Francesco D'Angelo, et al. ∙

research

∙ 11/14/2020

Factorized Gaussian Process Variational Autoencoders

Variational autoencoders often assume isotropic Gaussian priors and mean...

6 Metod Jazbec, et al. ∙

research

∙ 10/26/2020

Scalable Gaussian Process Variational Autoencoders

Conventional variational autoencoders fail in modeling correlations betw...

18 Metod Jazbec, et al. ∙

research

∙ 10/20/2020

Sparse Gaussian Process Variational Autoencoders

Large, multi-dimensional spatio-temporal datasets are omnipresent in mod...

16 Matthew Ashman, et al. ∙

research

∙ 02/13/2020

PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees

Meta-learning can successfully acquire useful inductive biases from data...

3 Jonas Rothfuss, et al. ∙

research

∙ 10/17/2019

Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations

Clustering high-dimensional data, such as images or biological measureme...

35 Andreas Kopf, et al. ∙

research

∙ 10/03/2019

Variational PSOM: Deep Probabilistic Clustering with Self-Organizing Maps

Generating visualizations and interpretations from high-dimensional data...

29 Laura Manduchi, et al. ∙

research

∙ 09/28/2019

Deep Multiple Instance Learning for Taxonomic Classification of Metagenomic read sets

Metagenomic studies have increasingly utilized sequencing technologies i...

0 Andreas Georgiou, et al. ∙

research

∙ 09/27/2019

MGP-AttTCN: An Interpretable Machine Learning Model for the Prediction of Sepsis

With a mortality rate of 5.4 million lives worldwide every year and a he...

0 Margherita Rosnati, et al. ∙

research

∙ 07/09/2019

Multivariate Time Series Imputation with Variational Autoencoders

Multivariate time series with missing values are common in many areas, f...

0 Vincent Fortuin, et al. ∙

research

∙ 01/23/2019

Deep Mean Functions for Meta-Learning in Gaussian Processes

Fitting machine learning models in the low-data limit is challenging. Th...

0 Vincent Fortuin, et al. ∙

research

∙ 10/24/2018

Scalable Gaussian Processes on Discrete Domains

Kernel methods on discrete domains have shown great promise for many cha...

0 Vincent Fortuin, et al. ∙

research

∙ 06/06/2018

Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series

Human professionals are often required to make decisions based on comple...

0 Vincent Fortuin, et al. ∙

Vincent Fortuin

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